naa a22 4500 510725775 CHVBK 20180411082934.0 cr unu---uuuuu 180411e20131001xx s 000 0 eng 10.1007/s11947-012-0914-6 doi (NATIONALLICENCE)springer-10.1007/s11947-012-0914-6 Modeling and Optimization of Electrodialytic Desalination of Fish Sauce Using Artificial Neural Networks and Genetic Algorithm [Elektronische Daten] [Nathamol Chindapan, Shyam Sablani, Naphaporn Chiewchan, Sakamon Devahastin] Electrodialysis (ED) has been proposed as a means to reduce sodium ion concentration in fish sauce. However, no information is so far available on the optimum condition to operate the ED process. Artificial neural network (ANN)-based models were therefore developed to predict the ED performance and changes in selected quality attributes of ED-treated fish sauce; optimum operating condition of the process was then determined via multi-objective optimization using genetic algorithm (MOGA). The optimal ANN models were able to predict the ED performance with R 2 = 0.995, fish sauce basic characteristics with R 2 = 0.992, and the concentrations of total aroma compounds and total amino acids, flavor difference, and saltiness of the treated fish sauce with R 2 = 0.999. Through the use of MOGA, the optimum condition of the ED process was the use of an applied voltage of 6.3V and the maintenance of the residual salt concentration of the treated fish sauce of 14.3% (w/w). Springer Science+Business Media, LLC, 2012 Low-sodium product nationallicence Multi-objective optimization nationallicence Process performance nationallicence Quality nationallicence Chindapan Nathamol Department of Food Engineering, Faculty of Engineering, King Mongkut's University of Technology Thonburi, 126 Pracha u-tid Road, Tungkru, 10140, Bangkok, Thailand aut Sablani Shyam Department of Biological Systems Engineering, Washington State University, 99164-6120, Pullman, WA, USA aut Chiewchan Naphaporn Department of Food Engineering, Faculty of Engineering, King Mongkut's University of Technology Thonburi, 126 Pracha u-tid Road, Tungkru, 10140, Bangkok, Thailand aut Devahastin Sakamon Department of Food Engineering, Faculty of Engineering, King Mongkut's University of Technology Thonburi, 126 Pracha u-tid Road, Tungkru, 10140, Bangkok, Thailand aut Food and Bioprocess Technology Springer US; http://www.springer-ny.com 6/10(2013-10-01), 2695-2707 1935-5130 6:10<2695 2013 6 11947 https://doi.org/10.1007/s11947-012-0914-6 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11947-012-0914-6 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Chindapan Nathamol Department of Food Engineering, Faculty of Engineering, King Mongkut's University of Technology Thonburi, 126 Pracha u-tid Road, Tungkru, 10140, Bangkok, Thailand aut NATIONALLICENCE 700 1- Sablani Shyam Department of Biological Systems Engineering, Washington State University, 99164-6120, Pullman, WA, USA aut NATIONALLICENCE 700 1- Chiewchan Naphaporn Department of Food Engineering, Faculty of Engineering, King Mongkut's University of Technology Thonburi, 126 Pracha u-tid Road, Tungkru, 10140, Bangkok, Thailand aut NATIONALLICENCE 700 1- Devahastin Sakamon Department of Food Engineering, Faculty of Engineering, King Mongkut's University of Technology Thonburi, 126 Pracha u-tid Road, Tungkru, 10140, Bangkok, Thailand aut NATIONALLICENCE 773 0- Food and Bioprocess Technology Springer US; http://www.springer-ny.com 6/10(2013-10-01), 2695-2707 1935-5130 6:10<2695 2013 6 11947 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer